Ecological impact due to the implementation of a modeled and optimized hybrid system

This paper presents a very alarming forecasts about our future and particularly for a medium and long-term future. That is to say, several actions are being carried out by different civil and state parties to deal with these very concrete threats. And it is within this framework that this paper fits, and its objective is to highlight the hybrid systems and more precisely the photovoltaic-wind hybrid systems coupled with storage batteries, as an efficient alternative to the classical means of electricity production. This work will adopt a method of obtaining results called performance evaluation, so this manuscript will present firstly the mathematical model of this hybrid system to best conceive what it is about, then as second part will determine the exact figures of the largest amount of carbon dioxide (CO2) that can be avoided using this technology and following a precise methodology, this can be applied to our situation, i.e., a simple house or to any other type of installation

Reduce state of charge estimation errors with an extended Kalman filter algorithm

Li-ion batteries (LiBs) are accurately estimated under varying operating conditions and external influences using extended Kalman filtering (EKF). Estimating the state of charge (SOC) is essential for enhancing battery efficiency, though complexities and unpredictability present obstacles. To address this issue, the paper proposes a second-order resistance-capacitance (RC) battery model and derives the EKF algorithm from it. The EKF approach is chosen for its ability to handle complex battery behaviors. Through extensive evaluation using a Simulink MATLAB program, the proposed EKF algorithm demonstrates remarkable accuracy and robustness in SOC estimation. The root mean square error (RMSE) analysis shows that SOC estimation errors range from only 0.30% to 2.47%, indicating substantial improvement over conventional methods. These results demonstrate the effectiveness of an EKF-based approach in overcoming external influences and providing precise SOC estimations to optimize battery management. In addition to enhancing battery performance, the results of the study may lead to the development of more reliable energy storage systems in the future. This will contribute to the wider adoption of LiBs in various applications

A new approach to solve the problem of partial shading in a photovoltaic system

<p>This paper introduces a novel global maximum power point (GMPP) tracking method that addresses the challenges of efficiency and power quality degradation in photovoltaic (PV) systems caused by inadequate tracking of the GMPP. The proposed approach employs a cuckoo search algorithm with proportional, integral, and derivative (CSPID). A bio-inspired optimization technique, to effectively track the GMPP under varying weather conditions. To demonstrate its effectiveness, the CSPID algorithm is comprehensively evaluated against two well-established methods, particle swarm optimization (PSO), and cuckoo search algorithm traditional (CSA). The evaluation includes three different scenarios with gradual changes in irradiance and temperature, these tests show the ability of the algorithm to handle the condition of partial shading. The results reveal that the CSPID method achieves an average tracking time of 0.098s and an average tracking efficiency of 99.62%, thereby significantly improving the efficiency and quality of photovoltaic energy production.</p&gt